Detecting changes in dynamic and complex acoustic environments

Boubenec, Yves; Lawlor, Jennifer; Górska, Urszula; Shamma, Shihab A.; Englitz, Bernhard

doi:10.7554/elife.24910

Cited by 29 publications

(61 citation statements)

References 71 publications

(102 reference statements)

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“…Knowing that change-point occurs in ⅔ of sequences here, the a priori probability of having encountered a change-point increases as the sequence unfolds in time. Accordingly, in random sequences, subjects’ false alarms increased with sequence duration, as also observed in a recent study 12 . A model lacking such an explicit representation of change-points would, by contrast, have a stable false alarm rate throughout the sequence 54 .…”

Section: Discussionsupporting

confidence: 86%

Rational arbitration between statistics and rules in human sequence processing

Maheu

Meyniel

Dehaene

2020

Preprint

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5Detecting and learning temporal regularities is essential to accurately predict the future. 6 Past research indicates that humans are sensitive to two types of sequential regularities: 7 deterministic rules, which afford sure predictions, and statistical biases, which govern the 8 probabilities of individual items and their transitions. How does the human brain arbitrate 9 between those two types? We used finger tracking to continuously monitor the online 10 build-up of evidence, confidence, false alarms and changes-of-mind during sequence 11 learning. All these aspects of behaviour conformed tightly to a hierarchical Bayesian 12 inference model with distinct hypothesis spaces for statistics and rules, yet linked by a 13 single probabilistic currency. Alternative models based either on a single statistical 14 mechanism or on two non-commensurable systems were rejected. Our results indicate 15 that a hierarchical Bayesian inference mechanism, capable of operating over several 16 distinct hypothesis spaces, underlies the human capability to learn both statistics and 17 rules.

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Section: Discussionsupporting

confidence: 86%

Rational arbitration between statistics and rules in human sequence processing

Maheu

Meyniel

Dehaene

2020

Preprint

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“…Note that in traditional "go-no go" (or "yes-no") tasks, false alarms correspond to "go" responses on "no-go" trials. Our definition extends false alarms to "go" responses that occur before the "go" signal, in line with previous change detection studies (Boubenec et al 2017;Cook and Maunsell 2002). When the subjects removed their finger from the port, the LED light would turn off and the click sequence would continue to its programmed conclusion.…”

Section: Methodssupporting

confidence: 52%

“…This generalization also leads to the suggestion that alteration of decision bound in our change detection task may involve similar neural circuits and mechanisms that are involved in discrimination tasks. Models that trigger choices with bounds on neural responses can explain behavior in both change detection (Boubenec et al 2017;Cook and Maunsell 2002) and discrimination tasks (Gold and Shadlen 2007;Hanks and Summerfield 2017). Candidate neural responses involved in this bound-crossing process span a wide range of brain regions, including parietal cortex, frontal cortex, and subcortical structures (Brody and Hanks 2016;Gold and Shadlen 2007).…”

Section: Discussionmentioning

confidence: 99%

Characterization of decision commitment rule alterations during an auditory change detection task

Johnson

Verma

Sun

2017

Journal of Neurophysiology

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A critical component of decision making is determining when to commit to a choice. This involves stopping rules that specify the requirements for decision commitment. Flexibility of decision stopping rules provides an important means of control over decision-making processes. In many situations, these stopping rules establish a balance between premature decisions and late decisions. In this study we use a novel change detection paradigm to examine how subjects control this balance when invoking different decision stopping rules. The task design allows us to estimate the temporal weighting of sensory information for the decisions, and we find that different stopping rules did not result in systematic differences in that weighting. We also find bidirectional post-error alterations of decision strategy that depend on the type of error and effectively reduce the probability of making consecutive mistakes of the same type. This is a generalization to change detection tasks of the widespread observation of unidirectional post-error slowing in forced-choice tasks. On the basis of these results, we suggest change detection tasks as a promising paradigm to study the neural mechanisms that support flexible control of decision rules. Flexible decision stopping rules confer control over decision processes. Using an auditory change detection task, we found that alterations of decision stopping rules did not result in systematic changes in the temporal weighting of sensory information. We also found that post-error alterations of decision stopping rules depended on the type of mistake subjects make. These results provide guidance for understanding the neural mechanisms that control decision stopping rules, one of the critical components of decision making and behavioral flexibility.

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“…Do listeners retain any sense of drift in statistical properties when they change over time [29]? One presumptive advantage of a limited averaging window is to retain some sensitivity to such changes.…”

Section: Discussionmentioning

confidence: 99%

Adaptive and Selective Time Averaging of Auditory Scenes

McWalter

McDermott

2018

Current Biology

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To overcome variability, estimate scene characteristics, and compress sensory input, perceptual systems pool data into statistical summaries. Despite growing evidence for statistical representations in perception, the underlying mechanisms remain poorly understood. One example of such representations occurs in auditory scenes, where background texture appears to be represented with time-averaged sound statistics. We probed the averaging mechanism using "texture steps"-textures containing subtle shifts in stimulus statistics. Although generally imperceptible, steps occurring in the previous several seconds biased texture judgments, indicative of a multi-second averaging window. Listeners seemed unable to willfully extend or restrict this window but showed signatures of longer integration times for temporally variable textures. In all cases the measured timescales were substantially longer than previously reported integration times in the auditory system. Integration also showed signs of being restricted to sound elements attributed to a common source. The results suggest an integration process that depends on stimulus characteristics, integrating over longer extents when it benefits statistical estimation of variable signals and selectively integrating stimulus components likely to have a common cause in the world. Our methodology could be naturally extended to examine statistical representations of other types of sensory signals.

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Detecting changes in dynamic and complex acoustic environments

Cited by 29 publications

References 71 publications

Rational arbitration between statistics and rules in human sequence processing

Rational arbitration between statistics and rules in human sequence processing

Characterization of decision commitment rule alterations during an auditory change detection task

Adaptive and Selective Time Averaging of Auditory Scenes

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